Chierichetti et al. [1]} introduced the fair clustering problem with the aim to ensure equal representation for each protected attribute, such as gender, in every cluster. In their formulation, each instance is assigned with one of two colors (red, blue). They proposed a two-phase approach: clustering all instances into fairlets - small clusters preserving the fairness measure, and then applying vanilla clustering methods (\(k\) -Center, \(k\) -Median) on those fairlets.
Subsequent studies focus on generalization and scalability. Backurs et al. [2]} presented an approximate fairlet decomposition algorithm which can formulate the fairlets in nearly linear time thus tackling the efficiency bottleneck of [1]}.
Rösner and Schmidt [4]} generalize the fair clustering problem to more than two protected attributes. A more generalized and tunable notion of fairness for clustering was introduced in Bera et al. [5]}.
Anshuman and Prasant [6]} introduced a fair hierarchical agglomerative clustering method for multiple protected attributes.
